Overview

Brought to you by YData

Dataset statistics

Number of variables38
Number of observations18923
Missing cells0
Missing cells (%)0.0%
Duplicate rows1250
Duplicate rows (%)6.6%
Total size in memory3.6 MiB
Average record size in memory200.0 B

Variable types

Numeric7
Categorical31

Alerts

Dataset has 1250 (6.6%) duplicate rowsDuplicates
4x4 is highly overall correlated with Cylinders and 1 other fieldsHigh correlation
Airbags is highly overall correlated with South KoreaHigh correlation
Automatic is highly overall correlated with ManualHigh correlation
Cylinders is highly overall correlated with 4x4 and 2 other fieldsHigh correlation
Diesel is highly overall correlated with PetrolHigh correlation
Engine volume int is highly overall correlated with Cylinders and 1 other fieldsHigh correlation
Front is highly overall correlated with 4x4 and 2 other fieldsHigh correlation
Hybrid is highly overall correlated with PetrolHigh correlation
Jeep is highly overall correlated with SedanHigh correlation
Manual is highly overall correlated with Automatic and 1 other fieldsHigh correlation
Petrol is highly overall correlated with Diesel and 1 other fieldsHigh correlation
Prod. year is highly overall correlated with Manual and 1 other fieldsHigh correlation
Russia is highly overall correlated with Prod. yearHigh correlation
Sedan is highly overall correlated with JeepHigh correlation
South Korea is highly overall correlated with AirbagsHigh correlation
Wheel is highly imbalanced (60.9%)Imbalance
China is highly imbalanced (99.8%)Imbalance
France is highly imbalanced (96.8%)Imbalance
Italy is highly imbalanced (95.7%)Imbalance
Russia is highly imbalanced (96.2%)Imbalance
Sweden is highly imbalanced (98.8%)Imbalance
UK is highly imbalanced (93.5%)Imbalance
Coupe is highly imbalanced (80.7%)Imbalance
Goods wagon is highly imbalanced (90.6%)Imbalance
Microbus is highly imbalanced (88.3%)Imbalance
Minivan is highly imbalanced (78.9%)Imbalance
Pickup is highly imbalanced (97.3%)Imbalance
Universal is highly imbalanced (86.4%)Imbalance
LPG is highly imbalanced (72.7%)Imbalance
Turbo is highly imbalanced (53.1%)Imbalance
Manual is highly imbalanced (53.9%)Imbalance
Variator is highly imbalanced (76.4%)Imbalance
Price is highly skewed (γ1 = 135.3841522)Skewed
Mileage is highly skewed (γ1 = 38.57749563)Skewed
Levy has 5708 (30.2%) zerosZeros
Mileage has 714 (3.8%) zerosZeros
Airbags has 2384 (12.6%) zerosZeros

Reproduction

Analysis started2024-10-03 12:42:35.369537
Analysis finished2024-10-03 12:42:43.034517
Duration7.66 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Price
Real number (ℝ)

SKEWED 

Distinct2315
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18587.34
Minimum1
Maximum26307500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size295.7 KiB
2024-10-03T15:42:43.242657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile314
Q15331
median13172
Q322063
95-th percentile49417.2
Maximum26307500
Range26307499
Interquartile range (IQR)16732

Descriptive statistics

Standard deviation192140.71
Coefficient of variation (CV)10.337181
Kurtosis18523.112
Mean18587.34
Median Absolute Deviation (MAD)8311
Skewness135.38415
Sum3.5172824 × 108
Variance3.6918051 × 1010
MonotonicityNot monotonic
2024-10-03T15:42:43.342723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15681 274
 
1.4%
470 264
 
1.4%
392 237
 
1.3%
14113 236
 
1.2%
10976 233
 
1.2%
314 232
 
1.2%
12544 221
 
1.2%
7840 220
 
1.2%
18817 213
 
1.1%
17249 213
 
1.1%
Other values (2305) 16580
87.6%
ValueCountFrequency (%)
1 2
 
< 0.1%
3 15
 
0.1%
6 6
 
< 0.1%
9 1
 
< 0.1%
19 1
 
< 0.1%
20 7
 
< 0.1%
25 16
 
0.1%
28 1
 
< 0.1%
30 77
0.4%
31 13
 
0.1%
ValueCountFrequency (%)
26307500 1
< 0.1%
872946 1
< 0.1%
627220 1
< 0.1%
308906 1
< 0.1%
297930 2
< 0.1%
288521 1
< 0.1%
260296 1
< 0.1%
254024 1
< 0.1%
250574 1
< 0.1%
228935 1
< 0.1%

Levy
Real number (ℝ)

ZEROS 

Distinct559
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean632.91994
Minimum0
Maximum11714
Zeros5708
Zeros (%)30.2%
Negative0
Negative (%)0.0%
Memory size295.7 KiB
2024-10-03T15:42:43.442796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median642
Q3917
95-th percentile1537
Maximum11714
Range11714
Interquartile range (IQR)917

Descriptive statistics

Standard deviation568.27013
Coefficient of variation (CV)0.89785468
Kurtosis29.586319
Mean632.91994
Median Absolute Deviation (MAD)382
Skewness2.4038806
Sum11976744
Variance322930.94
MonotonicityNot monotonic
2024-10-03T15:42:43.542515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5708
30.2%
765 482
 
2.5%
891 453
 
2.4%
639 403
 
2.1%
640 398
 
2.1%
781 294
 
1.6%
1017 291
 
1.5%
707 268
 
1.4%
642 259
 
1.4%
836 259
 
1.4%
Other values (549) 10108
53.4%
ValueCountFrequency (%)
0 5708
30.2%
87 10
 
0.1%
115 1
 
< 0.1%
155 7
 
< 0.1%
167 2
 
< 0.1%
173 1
 
< 0.1%
175 1
 
< 0.1%
247 6
 
< 0.1%
259 47
 
0.2%
271 29
 
0.2%
ValueCountFrequency (%)
11714 2
< 0.1%
11706 1
< 0.1%
7536 1
< 0.1%
7063 1
< 0.1%
7058 1
< 0.1%
5908 1
< 0.1%
5877 1
< 0.1%
5681 1
< 0.1%
5679 2
< 0.1%
5666 1
< 0.1%

Prod. year
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.9142
Minimum1939
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size295.7 KiB
2024-10-03T15:42:43.639845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1939
5-th percentile1999
Q12009
median2012
Q32015
95-th percentile2017
Maximum2020
Range81
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.6658934
Coefficient of variation (CV)0.002817571
Kurtosis11.449452
Mean2010.9142
Median Absolute Deviation (MAD)3
Skewness-2.0885162
Sum38052529
Variance32.102348
MonotonicityNot monotonic
2024-10-03T15:42:43.909208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2012 2130
11.3%
2014 2090
11.0%
2013 1913
10.1%
2011 1582
 
8.4%
2015 1527
 
8.1%
2010 1463
 
7.7%
2016 1449
 
7.7%
2017 941
 
5.0%
2008 731
 
3.9%
2009 595
 
3.1%
Other values (44) 4502
23.8%
ValueCountFrequency (%)
1939 3
< 0.1%
1943 1
 
< 0.1%
1947 1
 
< 0.1%
1953 4
< 0.1%
1957 1
 
< 0.1%
1964 2
< 0.1%
1965 2
< 0.1%
1968 1
 
< 0.1%
1973 1
 
< 0.1%
1974 1
 
< 0.1%
ValueCountFrequency (%)
2020 47
 
0.2%
2019 304
 
1.6%
2018 491
 
2.6%
2017 941
5.0%
2016 1449
7.7%
2015 1527
8.1%
2014 2090
11.0%
2013 1913
10.1%
2012 2130
11.3%
2011 1582
8.4%

Leather interior
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
13730 
1
5193 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13730
72.6%
1 5193
 
27.4%

Length

2024-10-03T15:42:44.006270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:44.093920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 13730
72.6%
1 5193
 
27.4%

Most occurring characters

ValueCountFrequency (%)
0 13730
72.6%
1 5193
 
27.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 13730
72.6%
1 5193
 
27.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 13730
72.6%
1 5193
 
27.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 13730
72.6%
1 5193
 
27.4%

Mileage
Real number (ℝ)

SKEWED  ZEROS 

Distinct7687
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1555448.7
Minimum0
Maximum2.1474836 × 109
Zeros714
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size295.7 KiB
2024-10-03T15:42:44.176059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2222
Q170195.5
median126400
Q3189135.5
95-th percentile320554
Maximum2.1474836 × 109
Range2.1474836 × 109
Interquartile range (IQR)118940

Descriptive statistics

Standard deviation48803494
Coefficient of variation (CV)31.375829
Kurtosis1572.5888
Mean1555448.7
Median Absolute Deviation (MAD)59019
Skewness38.577496
Sum2.9433757 × 1010
Variance2.381781 × 1015
MonotonicityNot monotonic
2024-10-03T15:42:44.276015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 714
 
3.8%
200000 181
 
1.0%
150000 159
 
0.8%
160000 120
 
0.6%
180000 117
 
0.6%
100000 105
 
0.6%
1000 100
 
0.5%
170000 99
 
0.5%
120000 98
 
0.5%
130000 84
 
0.4%
Other values (7677) 17146
90.6%
ValueCountFrequency (%)
0 714
3.8%
13 1
 
< 0.1%
18 1
 
< 0.1%
21 1
 
< 0.1%
98 1
 
< 0.1%
102 1
 
< 0.1%
120 2
 
< 0.1%
261 1
 
< 0.1%
429 1
 
< 0.1%
498 1
 
< 0.1%
ValueCountFrequency (%)
2147483647 7
< 0.1%
1777777778 1
 
< 0.1%
1234567899 1
 
< 0.1%
1111111111 2
 
< 0.1%
999999999 5
< 0.1%
777777777 1
 
< 0.1%
222222222 1
 
< 0.1%
111111111 1
 
< 0.1%
58008888 1
 
< 0.1%
55556665 1
 
< 0.1%

Cylinders
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5801406
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size295.7 KiB
2024-10-03T15:42:44.342478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14
median4
Q34
95-th percentile8
Maximum16
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.20021
Coefficient of variation (CV)0.26204654
Kurtosis6.5885006
Mean4.5801406
Median Absolute Deviation (MAD)0
Skewness2.1079855
Sum86670
Variance1.440504
MonotonicityNot monotonic
2024-10-03T15:42:44.426201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
4 14159
74.8%
6 3372
 
17.8%
8 976
 
5.2%
5 169
 
0.9%
3 107
 
0.6%
2 42
 
0.2%
12 38
 
0.2%
1 37
 
0.2%
10 12
 
0.1%
16 5
 
< 0.1%
Other values (3) 6
 
< 0.1%
ValueCountFrequency (%)
1 37
 
0.2%
2 42
 
0.2%
3 107
 
0.6%
4 14159
74.8%
5 169
 
0.9%
6 3372
 
17.8%
7 4
 
< 0.1%
8 976
 
5.2%
9 1
 
< 0.1%
10 12
 
0.1%
ValueCountFrequency (%)
16 5
 
< 0.1%
14 1
 
< 0.1%
12 38
 
0.2%
10 12
 
0.1%
9 1
 
< 0.1%
8 976
 
5.2%
7 4
 
< 0.1%
6 3372
 
17.8%
5 169
 
0.9%
4 14159
74.8%

Wheel
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
17470 
1
 
1453

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 17470
92.3%
1 1453
 
7.7%

Length

2024-10-03T15:42:44.509385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:44.576100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 17470
92.3%
1 1453
 
7.7%

Most occurring characters

ValueCountFrequency (%)
0 17470
92.3%
1 1453
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 17470
92.3%
1 1453
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 17470
92.3%
1 1453
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 17470
92.3%
1 1453
 
7.7%

Airbags
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5683031
Minimum0
Maximum16
Zeros2384
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size295.7 KiB
2024-10-03T15:42:44.638410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median6
Q312
95-th percentile12
Maximum16
Range16
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.3224248
Coefficient of variation (CV)0.65807329
Kurtosis-1.3321663
Mean6.5683031
Median Absolute Deviation (MAD)4
Skewness0.086392834
Sum124292
Variance18.683356
MonotonicityNot monotonic
2024-10-03T15:42:44.718884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
4 5733
30.3%
12 5534
29.2%
0 2384
12.6%
8 1568
 
8.3%
6 1289
 
6.8%
2 1051
 
5.6%
10 845
 
4.5%
5 104
 
0.5%
16 93
 
0.5%
7 85
 
0.4%
Other values (7) 237
 
1.3%
ValueCountFrequency (%)
0 2384
12.6%
1 76
 
0.4%
2 1051
 
5.6%
3 37
 
0.2%
4 5733
30.3%
5 104
 
0.5%
6 1289
 
6.8%
7 85
 
0.4%
8 1568
 
8.3%
9 62
 
0.3%
ValueCountFrequency (%)
16 93
 
0.5%
15 7
 
< 0.1%
14 20
 
0.1%
13 2
 
< 0.1%
12 5534
29.2%
11 33
 
0.2%
10 845
 
4.5%
9 62
 
0.3%
8 1568
 
8.3%
7 85
 
0.4%

China
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
18921 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18921
> 99.9%
1 2
 
< 0.1%

Length

2024-10-03T15:42:44.809284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:44.875823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18921
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 18921
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18921
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18921
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18921
> 99.9%
1 2
 
< 0.1%

France
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
18861 
1
 
62

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18861
99.7%
1 62
 
0.3%

Length

2024-10-03T15:42:44.942645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:45.009893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18861
99.7%
1 62
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 18861
99.7%
1 62
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18861
99.7%
1 62
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18861
99.7%
1 62
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18861
99.7%
1 62
 
0.3%

Germany
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
14571 
1
4352 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14571
77.0%
1 4352
 
23.0%

Length

2024-10-03T15:42:45.092384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:45.159467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14571
77.0%
1 4352
 
23.0%

Most occurring characters

ValueCountFrequency (%)
0 14571
77.0%
1 4352
 
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14571
77.0%
1 4352
 
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14571
77.0%
1 4352
 
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14571
77.0%
1 4352
 
23.0%

Italy
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
18835 
1
 
88

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18835
99.5%
1 88
 
0.5%

Length

2024-10-03T15:42:45.256240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:45.326006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18835
99.5%
1 88
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 18835
99.5%
1 88
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18835
99.5%
1 88
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18835
99.5%
1 88
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18835
99.5%
1 88
 
0.5%

Japan
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
11907 
1
7016 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 11907
62.9%
1 7016
37.1%

Length

2024-10-03T15:42:45.409489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:45.488484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 11907
62.9%
1 7016
37.1%

Most occurring characters

ValueCountFrequency (%)
0 11907
62.9%
1 7016
37.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11907
62.9%
1 7016
37.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11907
62.9%
1 7016
37.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11907
62.9%
1 7016
37.1%

Russia
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
18846 
1
 
77

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18846
99.6%
1 77
 
0.4%

Length

2024-10-03T15:42:45.567715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:45.642831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18846
99.6%
1 77
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 18846
99.6%
1 77
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18846
99.6%
1 77
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18846
99.6%
1 77
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18846
99.6%
1 77
 
0.4%

South Korea
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
14248 
1
4675 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14248
75.3%
1 4675
 
24.7%

Length

2024-10-03T15:42:45.709420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:45.783587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14248
75.3%
1 4675
 
24.7%

Most occurring characters

ValueCountFrequency (%)
0 14248
75.3%
1 4675
 
24.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14248
75.3%
1 4675
 
24.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14248
75.3%
1 4675
 
24.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14248
75.3%
1 4675
 
24.7%

Sweden
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
18902 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18902
99.9%
1 21
 
0.1%

Length

2024-10-03T15:42:45.859133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:45.918952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18902
99.9%
1 21
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 18902
99.9%
1 21
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18902
99.9%
1 21
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18902
99.9%
1 21
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18902
99.9%
1 21
 
0.1%

UK
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
18778 
1
 
145

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18778
99.2%
1 145
 
0.8%

Length

2024-10-03T15:42:45.992241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:46.059303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18778
99.2%
1 145
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 18778
99.2%
1 145
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18778
99.2%
1 145
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18778
99.2%
1 145
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18778
99.2%
1 145
 
0.8%

USA
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
16462 
1
2461 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 16462
87.0%
1 2461
 
13.0%

Length

2024-10-03T15:42:46.135580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:46.199999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 16462
87.0%
1 2461
 
13.0%

Most occurring characters

ValueCountFrequency (%)
0 16462
87.0%
1 2461
 
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 16462
87.0%
1 2461
 
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 16462
87.0%
1 2461
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 16462
87.0%
1 2461
 
13.0%

Coupe
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
18360 
1
 
563

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18360
97.0%
1 563
 
3.0%

Length

2024-10-03T15:42:46.276031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:46.342835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18360
97.0%
1 563
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 18360
97.0%
1 563
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18360
97.0%
1 563
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18360
97.0%
1 563
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18360
97.0%
1 563
 
3.0%

Goods wagon
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
18694 
1
 
229

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18694
98.8%
1 229
 
1.2%

Length

2024-10-03T15:42:46.409059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:46.488662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18694
98.8%
1 229
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 18694
98.8%
1 229
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18694
98.8%
1 229
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18694
98.8%
1 229
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18694
98.8%
1 229
 
1.2%

Hatchback
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
16124 
1
2799 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 16124
85.2%
1 2799
 
14.8%

Length

2024-10-03T15:42:46.559450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:46.625910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 16124
85.2%
1 2799
 
14.8%

Most occurring characters

ValueCountFrequency (%)
0 16124
85.2%
1 2799
 
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 16124
85.2%
1 2799
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 16124
85.2%
1 2799
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 16124
85.2%
1 2799
 
14.8%

Jeep
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
13545 
1
5378 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 13545
71.6%
1 5378
 
28.4%

Length

2024-10-03T15:42:46.709160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:46.776123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 13545
71.6%
1 5378
 
28.4%

Most occurring characters

ValueCountFrequency (%)
0 13545
71.6%
1 5378
 
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 13545
71.6%
1 5378
 
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 13545
71.6%
1 5378
 
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 13545
71.6%
1 5378
 
28.4%

Microbus
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
18624 
1
 
299

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18624
98.4%
1 299
 
1.6%

Length

2024-10-03T15:42:46.859105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:46.921681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18624
98.4%
1 299
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 18624
98.4%
1 299
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18624
98.4%
1 299
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18624
98.4%
1 299
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18624
98.4%
1 299
 
1.6%

Minivan
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
18290 
1
 
633

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18290
96.7%
1 633
 
3.3%

Length

2024-10-03T15:42:46.992469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:47.059392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18290
96.7%
1 633
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 18290
96.7%
1 633
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18290
96.7%
1 633
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18290
96.7%
1 633
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18290
96.7%
1 633
 
3.3%

Pickup
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
18872 
1
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18872
99.7%
1 51
 
0.3%

Length

2024-10-03T15:42:47.135657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:47.199956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18872
99.7%
1 51
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 18872
99.7%
1 51
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18872
99.7%
1 51
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18872
99.7%
1 51
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18872
99.7%
1 51
 
0.3%

Sedan
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
10324 
1
8599 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10324
54.6%
1 8599
45.4%

Length

2024-10-03T15:42:47.275816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:47.342571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10324
54.6%
1 8599
45.4%

Most occurring characters

ValueCountFrequency (%)
0 10324
54.6%
1 8599
45.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10324
54.6%
1 8599
45.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10324
54.6%
1 8599
45.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10324
54.6%
1 8599
45.4%

Universal
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
18562 
1
 
361

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18562
98.1%
1 361
 
1.9%

Length

2024-10-03T15:42:47.424425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:47.492321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18562
98.1%
1 361
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 18562
98.1%
1 361
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18562
98.1%
1 361
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18562
98.1%
1 361
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18562
98.1%
1 361
 
1.9%

Diesel
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
14922 
1
4001 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14922
78.9%
1 4001
 
21.1%

Length

2024-10-03T15:42:47.568614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:47.625719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14922
78.9%
1 4001
 
21.1%

Most occurring characters

ValueCountFrequency (%)
0 14922
78.9%
1 4001
 
21.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14922
78.9%
1 4001
 
21.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14922
78.9%
1 4001
 
21.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14922
78.9%
1 4001
 
21.1%

Hybrid
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
15299 
1
3624 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 15299
80.8%
1 3624
 
19.2%

Length

2024-10-03T15:42:47.709238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:47.784449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15299
80.8%
1 3624
 
19.2%

Most occurring characters

ValueCountFrequency (%)
0 15299
80.8%
1 3624
 
19.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15299
80.8%
1 3624
 
19.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15299
80.8%
1 3624
 
19.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15299
80.8%
1 3624
 
19.2%

LPG
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
18038 
1
 
885

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18038
95.3%
1 885
 
4.7%

Length

2024-10-03T15:42:47.856079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:47.925788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18038
95.3%
1 885
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 18038
95.3%
1 885
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18038
95.3%
1 885
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18038
95.3%
1 885
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18038
95.3%
1 885
 
4.7%

Petrol
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
1
9944 
0
8979 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 9944
52.5%
0 8979
47.5%

Length

2024-10-03T15:42:47.992672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:48.067168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9944
52.5%
0 8979
47.5%

Most occurring characters

ValueCountFrequency (%)
1 9944
52.5%
0 8979
47.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 9944
52.5%
0 8979
47.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 9944
52.5%
0 8979
47.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 9944
52.5%
0 8979
47.5%

Engine volume int
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3062464
Minimum0
Maximum20
Zeros10
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size295.7 KiB
2024-10-03T15:42:48.145325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.4
Q11.8
median2
Q32.5
95-th percentile4
Maximum20
Range20
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.87761392
Coefficient of variation (CV)0.3805378
Kurtosis19.554616
Mean2.3062464
Median Absolute Deviation (MAD)0.4
Skewness2.2080112
Sum43641.1
Variance0.7702062
MonotonicityNot monotonic
2024-10-03T15:42:48.257511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 4259
22.5%
2.5 2337
12.4%
1.8 1918
10.1%
1.6 1562
 
8.3%
1.5 1354
 
7.2%
3.5 1219
 
6.4%
3 1086
 
5.7%
2.4 1026
 
5.4%
1.3 534
 
2.8%
1.4 517
 
2.7%
Other values (55) 3111
16.4%
ValueCountFrequency (%)
0 10
 
0.1%
0.1 4
 
< 0.1%
0.2 10
 
0.1%
0.3 3
 
< 0.1%
0.4 23
0.1%
0.5 1
 
< 0.1%
0.6 7
 
< 0.1%
0.7 25
0.1%
0.8 9
 
< 0.1%
0.9 2
 
< 0.1%
ValueCountFrequency (%)
20 2
 
< 0.1%
7.3 1
 
< 0.1%
6.8 1
 
< 0.1%
6.7 1
 
< 0.1%
6.4 2
 
< 0.1%
6.3 7
 
< 0.1%
6.2 24
0.1%
6 5
 
< 0.1%
5.9 3
 
< 0.1%
5.8 1
 
< 0.1%

Turbo
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0.0
17031 
1.0
1892 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters56769
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 17031
90.0%
1.0 1892
 
10.0%

Length

2024-10-03T15:42:48.353405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:48.417238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 17031
90.0%
1.0 1892
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 35954
63.3%
. 18923
33.3%
1 1892
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56769
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 35954
63.3%
. 18923
33.3%
1 1892
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56769
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 35954
63.3%
. 18923
33.3%
1 1892
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56769
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 35954
63.3%
. 18923
33.3%
1 1892
 
3.3%

Automatic
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
1
13282 
0
5641 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 13282
70.2%
0 5641
29.8%

Length

2024-10-03T15:42:48.698366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:48.759282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 13282
70.2%
0 5641
29.8%

Most occurring characters

ValueCountFrequency (%)
1 13282
70.2%
0 5641
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 13282
70.2%
0 5641
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 13282
70.2%
0 5641
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 13282
70.2%
0 5641
29.8%

Manual
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
17079 
1
1844 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 17079
90.3%
1 1844
 
9.7%

Length

2024-10-03T15:42:48.843002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:48.909093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 17079
90.3%
1 1844
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 17079
90.3%
1 1844
 
9.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 17079
90.3%
1 1844
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 17079
90.3%
1 1844
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 17079
90.3%
1 1844
 
9.7%

Variator
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
18190 
1
 
733

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18190
96.1%
1 733
 
3.9%

Length

2024-10-03T15:42:48.985954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:49.043437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18190
96.1%
1 733
 
3.9%

Most occurring characters

ValueCountFrequency (%)
0 18190
96.1%
1 733
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18190
96.1%
1 733
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18190
96.1%
1 733
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18190
96.1%
1 733
 
3.9%

4x4
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
0
14954 
1
3969 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 14954
79.0%
1 3969
 
21.0%

Length

2024-10-03T15:42:49.126105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:49.202399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14954
79.0%
1 3969
 
21.0%

Most occurring characters

ValueCountFrequency (%)
0 14954
79.0%
1 3969
 
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14954
79.0%
1 3969
 
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14954
79.0%
1 3969
 
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14954
79.0%
1 3969
 
21.0%

Front
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
1
12694 
0
6229 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 12694
67.1%
0 6229
32.9%

Length

2024-10-03T15:42:49.275906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T15:42:49.344032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 12694
67.1%
0 6229
32.9%

Most occurring characters

ValueCountFrequency (%)
1 12694
67.1%
0 6229
32.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 12694
67.1%
0 6229
32.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 12694
67.1%
0 6229
32.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 12694
67.1%
0 6229
32.9%

Interactions

2024-10-03T15:42:41.809432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:38.576252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:39.099921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:39.726295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:40.276173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:40.776247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:41.292584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:41.886177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:38.651726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:39.159418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:39.795762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:40.342350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:40.842870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:41.359321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:41.959421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:38.726433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:39.235998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:39.876220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:40.409381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:40.909276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:41.437242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:42.042738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:38.796013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:39.309412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:39.970894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:40.476134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:40.996157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:41.517666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:42.117277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:38.859606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:39.376268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:40.035209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:40.542937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:41.068562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:41.576117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:42.192536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:38.942939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:39.451756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:40.109423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:40.626665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:41.142322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:41.659969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:42.268747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:39.009390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:39.526325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:40.192590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:40.700006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:41.209341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T15:42:41.726178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-03T15:42:49.426087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
4x4AirbagsAutomaticChinaCoupeCylindersDieselEngine volume intFranceFrontGermanyGoods wagonHatchbackHybridItalyJapanJeepLPGLeather interiorLevyManualMicrobusMileageMinivanPetrolPickupPriceProd. yearRussiaSedanSouth KoreaSwedenTurboUKUSAUniversalVariatorWheel
4x41.0000.3260.0000.0000.0630.5230.0000.4330.0180.7350.1990.0460.1780.1020.0180.1420.4930.0580.0880.2950.0760.0560.0000.0570.0960.0520.0000.1070.0350.2510.2710.0000.0570.0610.1200.0230.0600.002
Airbags0.3261.0000.4240.0000.0510.2130.3080.2440.0450.3760.2610.1730.1480.2800.0450.2510.1670.2260.4670.1380.3840.268-0.0320.1480.1470.025-0.0530.1760.1150.2020.5470.0310.2260.0380.0650.1200.1530.316
Automatic0.0000.4241.0000.0070.0750.1180.0560.0580.0700.1770.3070.1600.0210.1110.0420.0730.1420.0630.3680.1210.5040.1870.0300.0070.1210.0240.0000.3510.0950.0000.2380.0090.3550.0500.0320.0110.3080.080
China0.0000.0000.0071.0000.0000.0760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Coupe0.0630.0510.0750.0001.0000.0780.0650.0600.0000.0830.0910.0160.0720.0760.1180.0820.1100.0330.0180.0370.0740.0200.0000.0310.1320.0000.0000.0820.0000.1590.0250.0040.0260.0540.0000.0220.0320.014
Cylinders0.5230.2130.1180.0760.0781.0000.1220.6890.0200.6430.4460.0760.2050.1380.0210.0950.2570.0480.2100.1830.1150.1230.1580.0480.1670.031-0.030-0.1620.0560.0840.3010.1320.1000.0490.1000.0570.0950.092
Diesel0.0000.3080.0560.0000.0650.1221.0000.1650.0320.0480.1500.1340.1870.2520.0240.3680.2530.1140.1140.0500.1350.2270.0000.1760.5450.0390.0000.0840.0270.2550.2570.0020.1940.0180.0070.0980.1030.132
Engine volume int0.4330.2440.0580.0000.0600.6890.1651.0000.0420.5190.2290.0400.3430.1100.0450.0810.2370.1150.1960.3390.0710.1330.1590.1600.1790.0420.056-0.0400.0290.0860.2220.0290.0200.0340.0590.0390.1210.144
France0.0180.0450.0700.0000.0000.0200.0320.0421.0000.0300.0290.0570.0260.0230.0000.0420.0240.0080.0650.0000.1290.0000.0000.0010.0000.0000.0000.0500.0000.0240.0310.0000.0590.0000.0190.0490.0050.000
Front0.7350.3760.1770.0000.0830.6430.0480.5190.0301.0000.4880.0150.2500.2010.0280.0210.2960.0780.0470.2820.1020.1230.0050.0470.1200.0550.0000.2560.0740.1250.3740.0000.1880.0530.1150.0360.1000.022
Germany0.1990.2610.3070.0000.0910.4460.1500.2290.0290.4881.0000.0860.1370.2570.0360.4190.0580.0600.0250.0670.2470.0360.0260.0160.0610.0190.0000.3010.0330.0990.3130.0150.2640.0470.2110.0000.1050.087
Goods wagon0.0460.1730.1600.0000.0160.0760.1340.0400.0570.0150.0861.0000.0450.0530.0000.0730.0690.0150.1660.0000.3100.0100.0000.0180.0700.0000.0320.1530.0000.1000.0590.0000.1510.0000.0620.0120.0200.001
Hatchback0.1780.1480.0210.0000.0720.2050.1870.3430.0260.2500.1370.0451.0000.3020.0240.3140.2620.0650.2450.0960.0040.0520.0000.0770.0470.0190.0000.0470.0140.3800.1980.0090.0860.0040.0290.0570.2540.281
Hybrid0.1020.2800.1110.0000.0760.1380.2520.1100.0230.2010.2570.0530.3021.0000.0310.4140.1320.1070.0350.0400.1570.0610.0000.0810.5120.0230.0000.1710.0290.0000.2050.0120.1490.0410.0200.0260.2100.002
Italy0.0180.0450.0420.0000.1180.0210.0240.0450.0000.0280.0360.0000.0240.0311.0000.0510.0200.0110.0000.0050.0540.0000.0000.0000.0560.0000.0000.0240.0000.0330.0380.0000.0130.0000.0240.0000.0020.010
Japan0.1420.2510.0730.0000.0820.0950.3680.0810.0420.0210.4190.0730.3140.4140.0511.0000.0150.1070.1950.1030.1590.0950.0000.0180.0230.0280.0000.0890.0480.1600.4400.0230.1670.0660.2970.0240.2250.296
Jeep0.4930.1670.1420.0000.1100.2570.2530.2370.0240.2960.0580.0690.2620.1320.0200.0151.0000.0930.1960.1620.1400.0790.0000.1170.0640.0310.0000.0730.0000.5750.0420.0000.0280.0430.0030.0870.0940.120
LPG0.0580.2260.0630.0000.0330.0480.1140.1150.0080.0780.0600.0150.0650.1070.0110.1070.0931.0000.0690.0830.0520.0240.0000.0120.2330.0000.0000.0660.0100.1490.2360.0000.0620.0130.0580.0000.0210.000
Leather interior0.0880.4670.3680.0000.0180.2100.1140.1960.0650.0470.0250.1660.2450.0350.0000.1950.1960.0691.0000.1140.3880.1630.0370.0560.0540.0300.0000.3670.0800.1000.2310.0000.1040.0340.0270.0320.1900.346
Levy0.2950.1380.1210.0000.0370.1830.0500.3390.0000.2820.0670.0000.0960.0400.0050.1030.1620.0830.1141.0000.0530.000-0.0470.0240.0710.0390.0020.3920.0130.0940.1490.0000.0630.0000.0310.0410.0380.084
Manual0.0760.3840.5040.0210.0740.1150.1350.0710.1290.1020.2470.3100.0040.1570.0540.1590.1400.0520.3880.0531.0000.3560.0460.0290.0180.0670.0070.5350.1850.0850.1680.0110.2510.0110.0490.0600.0650.027
Microbus0.0560.2680.1870.0000.0200.1230.2270.1330.0000.1230.0360.0100.0520.0610.0000.0950.0790.0240.1630.0000.3561.0000.0090.0210.1240.0000.0000.1640.0000.1150.0660.0000.2300.0050.1800.0140.0230.022
Mileage0.000-0.0320.0300.0000.0000.1580.0000.1590.0000.0050.0260.0000.0000.0000.0000.0000.0000.0000.037-0.0470.0460.0091.0000.0000.0000.000-0.205-0.3540.0380.0000.0000.0000.0000.0000.0000.0490.0000.024
Minivan0.0570.1480.0070.0000.0310.0480.1760.1600.0010.0470.0160.0180.0770.0810.0000.0180.1170.0120.0560.0240.0290.0210.0001.0000.1030.0000.0000.0840.0060.1690.0910.0000.0160.0130.0590.0240.0120.137
Petrol0.0960.1470.1210.0000.1320.1670.5450.1790.0000.1200.0610.0700.0470.5120.0560.0230.0640.2330.0540.0710.0180.1240.0000.1031.0000.0050.0000.0800.0410.1500.1200.0190.0000.0270.0080.0700.0640.078
Pickup0.0520.0250.0240.0000.0000.0310.0390.0420.0000.0550.0190.0000.0190.0230.0000.0280.0310.0000.0300.0390.0670.0000.0000.0000.0051.0000.0000.0000.0190.0460.0180.0000.0280.0000.0000.0000.0030.000
Price0.000-0.0530.0000.0000.000-0.0300.0000.0560.0000.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.0020.0070.000-0.2050.0000.0000.0001.0000.2950.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Prod. year0.1070.1760.3510.0000.082-0.1620.084-0.0400.0500.2560.3010.1530.0470.1710.0240.0890.0730.0660.3670.3920.5350.164-0.3540.0840.0800.0000.2951.0000.5180.1180.2330.0360.0820.0700.0580.0530.0960.238
Russia0.0350.1150.0950.0000.0000.0560.0270.0290.0000.0740.0330.0000.0140.0290.0000.0480.0000.0100.0800.0130.1850.0000.0380.0060.0410.0190.0000.5181.0000.0000.0350.0000.0050.0000.0220.0000.0080.015
Sedan0.2510.2020.0000.0000.1590.0840.2550.0860.0240.1250.0990.1000.3800.0000.0330.1600.5750.1490.1000.0940.0850.1150.0000.1690.1500.0460.0000.1180.0001.0000.0970.0000.0270.0390.0000.1270.0630.137
South Korea0.2710.5470.2380.0000.0250.3010.2570.2220.0310.3740.3130.0590.1980.2050.0380.4400.0420.2360.2310.1490.1680.0660.0000.0910.1200.0180.0000.2330.0350.0971.0000.0160.1530.0490.2210.0490.1130.162
Sweden0.0000.0310.0090.0000.0040.1320.0020.0290.0000.0000.0150.0000.0090.0120.0000.0230.0000.0000.0000.0000.0110.0000.0000.0000.0190.0000.0000.0360.0000.0000.0161.0000.0220.0000.0080.0000.0000.000
Turbo0.0570.2260.3550.0000.0260.1000.1940.0200.0590.1880.2640.1510.0860.1490.0130.1670.0280.0620.1040.0630.2510.2300.0000.0160.0000.0280.0000.0820.0050.0270.1530.0221.0000.0760.0660.0210.0570.030
UK0.0610.0380.0500.0000.0540.0490.0180.0340.0000.0530.0470.0000.0040.0410.0000.0660.0430.0130.0340.0000.0110.0050.0000.0130.0270.0000.0000.0700.0000.0390.0490.0000.0761.0000.0320.0000.0110.000
USA0.1200.0650.0320.0000.0000.1000.0070.0590.0190.1150.2110.0620.0290.0200.0240.2970.0030.0580.0270.0310.0490.1800.0000.0590.0080.0000.0000.0580.0220.0000.2210.0080.0660.0321.0000.0370.0330.098
Universal0.0230.1200.0110.0000.0220.0570.0980.0390.0490.0360.0000.0120.0570.0260.0000.0240.0870.0000.0320.0410.0600.0140.0490.0240.0700.0000.0000.0530.0000.1270.0490.0000.0210.0000.0371.0000.0050.035
Variator0.0600.1530.3080.0000.0320.0950.1030.1210.0050.1000.1050.0200.2540.2100.0020.2250.0940.0210.1900.0380.0650.0230.0000.0120.0640.0030.0000.0960.0080.0630.1130.0000.0570.0110.0330.0051.0000.208
Wheel0.0020.3160.0800.0000.0140.0920.1320.1440.0000.0220.0870.0010.2810.0020.0100.2960.1200.0000.3460.0840.0270.0220.0240.1370.0780.0000.0000.2380.0150.1370.1620.0000.0300.0000.0980.0350.2081.000

Missing values

2024-10-03T15:42:42.409297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-03T15:42:42.742726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PriceLevyProd. yearLeather interiorMileageCylindersWheelAirbagsChinaFranceGermanyItalyJapanRussiaSouth KoreaSwedenUKUSACoupeGoods wagonHatchbackJeepMicrobusMinivanPickupSedanUniversalDieselHybridLPGPetrolEngine volume intTurboAutomaticManualVariator4x4Front
0133281399201001860056.0012000010000000010000001003.50.010010
1166211018201111920006.008000000000100010000000013.00.000010
284670200612000004.012000010000000100000000011.30.000101
33607862201101689664.000000000000100010000001002.50.010010
41172644620140919014.004000010000000100000000011.30.010001
539493891201601609314.004000000100000010000010002.00.010001
61803761201002589094.0012000010000000100000001001.80.010001
7549751201302161184.0012000000100000000001000012.40.010001
81098394201403980694.0012000010000000000001001002.50.010001
9266570200701285006.0012000010000000010000000013.50.010010
PriceLevyProd. yearLeather interiorMileageCylindersWheelAirbagsChinaFranceGermanyItalyJapanRussiaSouth KoreaSwedenUKUSACoupeGoods wagonHatchbackJeepMicrobusMinivanPickupSedanUniversalDieselHybridLPGPetrolEngine volume intTurboAutomaticManualVariator4x4Front
19227297931053201402190306.0012001000000000000001010003.50.010010
192287061850200801228746.0012001000000000000001010003.50.010000
19229500200811500004.006000010000000100000001001.50.010001
19230470645201103073254.0012000010000000100000001001.80.010001
1923158021055201301078006.0012001000000000000001010003.50.010000
1923284670199903000004.005001000000010000000000002.01.001000
1923315681831201101616004.008000000100000000001000012.40.000001
1923426108836201001163654.004000000100000010000010002.00.010001
192355331128820070512584.004000000000100010000010002.00.010001
19236470753201201869234.0012000000100000000001001002.40.010001

Duplicate rows

Most frequently occurring

PriceLevyProd. yearLeather interiorMileageCylindersWheelAirbagsChinaFranceGermanyItalyJapanRussiaSouth KoreaSwedenUKUSACoupeGoods wagonHatchbackJeepMicrobusMinivanPickupSedanUniversalDieselHybridLPGPetrolEngine volume intTurboAutomaticManualVariator4x4Front# duplicates
9191881760920180350584.0012000010000000000001001002.50.01000124
132392781201203143734.0012000010000000000001001002.50.01000123
65811133394201401793814.0012000010000000000001001002.50.01000123
247784394201401023974.0012000010000000000001001002.50.01000122
4173607781201201565184.0012000010000000000001001002.50.01000122
4685018779201301905494.0012000010000000000001001002.50.01000122
3021098394201403980694.0012000010000000000001001002.50.01000121
259862394201401304784.0012000010000000000001001002.50.01000120
3923136781201201593794.0012000010000000000001001002.50.01000120
104314101720170268024.0012001000000000000001000012.00.01000019